All of these factors reduce the impact of potential data outliers and make the comparison of feature embedding values between the train and test sets more uniform. The Resnet-32 architecture [1]} that was used in our experiments meets these criteria (i.e., normalized pixel inputs, layers with batch normalization and ReLU activation function).
